Bibliographic record
Abstract
paragraph that describes the manuscript.The abstract is published at the beginning of an article and is also displayed in databases, such as PubMed and CINAHL.This is the text that individuals conducting literature searches see first.The abstract invites the potential reader to read the entire article.A well-written abstract improves the likelihood of an article being read and cited.Do not include the same sentences in the abstract that are in the introduction.Do not cite references in the abstract.Information on optimizing an abstract for search engines can be found at https://authorservices.wiley.com/ author-resources/Journal-Authors/Prepare/writing-for-seo.html.Manuscripts reporting original research, systematic reviews, integrative reviews, and other reviews conducted using a formal methodological process should include a structured abstract of no more than 300 words with the following headings:Introduction: State the purpose of the study or review and why this question is important.Methods: For original research, include the study design, setting (for example, location and level of clinical care), population intervention(s), and main outcome measure.For reviews, identify data sources, including years searched; inclusion and exclusion criteria used to select studies; and methods for abstracting data and assessing quality and validity.Results: State the key findings of the study or review.Include the response rate for surveys.Discussion: Clearly state the conclusions of the study or review, including the implications for clinical practice.Quality Improvement Report manuscripts should include a structured abstract of no more than 300 words with the following headings:Introduction: State the issue being addressed and the purpose of the project.Process: Describe the intervention and evaluation plan.Outcomes: Identify the key outcomes of the intervention.Discussion: State the conclusions of the project, including implications for clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.927 | 0.918 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".